From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: BNW using Octave instead of Matlab. This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02 --- sourcecodes/bnt-master/nethelp3.3/pca.htm | 51 +++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/pca.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/pca.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/pca.htm b/sourcecodes/bnt-master/nethelp3.3/pca.htm new file mode 100644 index 00000000..84fe3e38 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/pca.htm @@ -0,0 +1,51 @@ + + + +Netlab Reference Manual pca + + + +

pca +

+Purpose +

+Principal Components Analysis + +

+Synopsis +

+PCcoeff = pca(data)
+PCcoeff = pca(data, N)
+[PCcoeff, PCvec] = pca(data)
+

+ + +

+Description +

+ +PCcoeff = pca(data) computes the eigenvalues of the covariance +matrix of the dataset data and returns them as PCcoeff. These +coefficients give the variance of data along the corresponding +principal components. + +

PCcoeff = pca(data, N) returns the largest N eigenvalues. + +

[PCcoeff, PCvec] = pca(data) returns the principal components as +well as the coefficients. This is considerably more computationally +demanding than just computing the eigenvalues. + +

+See Also +

+eigdec, gtminit, ppca

+Pages: +Index +